app.py

import io
import json

import torch
import torch.nn as nn
import timm

from fastapi import FastAPI, File, UploadFile
from PIL import Image
from torchvision import transforms


# --------------------------------------------------
# Load model ONCE at startup
# --------------------------------------------------

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

WEIGHTS_PATH = "mobilenetv3_large_100_deploy.pth"
METADATA_PATH = "metadata.json"

with open(METADATA_PATH, "r") as f:
    metadata = json.load(f)

MODEL_NAME = metadata["model_name"]
NUM_CLASSES = metadata["num_classes"]
CLASS_NAMES = metadata["class_names"]

mean = metadata.get("normalize", {}).get("mean", [0.485, 0.456, 0.406])
std = metadata.get("normalize", {}).get("std", [0.229, 0.224, 0.225])
input_size = metadata.get("input_size", [224, 224])

# Build model (same as training: timm + num_classes=4)
model = timm.create_model(
    MODEL_NAME,
    pretrained=False,
    num_classes=NUM_CLASSES
)

# Load checkpoint safely.
# torch.load weights_only=True restricts unpickling to safer objects [page:1].
ckpt = torch.load(WEIGHTS_PATH, map_location=DEVICE, weights_only=True)

# Your training saved: {"model_name":..., "num_classes":..., "mean":..., "std":..., "state_dict":...}
# But some people save only state_dict, so support both:
state_dict = ckpt["state_dict"] if isinstance(ckpt, dict) and "state_dict" in ckpt else ckpt

model.load_state_dict(state_dict)
model.to(DEVICE)
model.eval()

# Preprocessing (must match training normalization)
transform = transforms.Compose([
    transforms.Resize((input_size[0], input_size[1])),
    transforms.ToTensor(),
    transforms.Normalize(mean=mean, std=std),
])


# --------------------------------------------------
# FastAPI app
# --------------------------------------------------

app = FastAPI(
    title="Age Group Classification API",
    description="MobileNetV3 Age-Group Prediction (A/B/C/D)",
    version="1.0"
)

@app.get("/")
def root():
    return {
        "message": "Age Group Classification API is running",
        "model": MODEL_NAME,
        "classes": CLASS_NAMES,
        "input_size": input_size,
        "normalize": {"mean": mean, "std": std}
    }

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    image_bytes = await file.read()
    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")

    image_tensor = transform(image).unsqueeze(0).to(DEVICE)

    with torch.no_grad():
        outputs = model(image_tensor)
        probs = torch.softmax(outputs, dim=1)

    conf, pred = torch.max(probs, dim=1)
    pred_idx = pred.item()

    # build probabilities dict for all classes (not hard-coded to 2)
    prob_dict = {
        CLASS_NAMES[i]: round(probs[0, i].item() * 100, 2)
        for i in range(NUM_CLASSES)
    }

    return {
        "predicted_class": CLASS_NAMES[pred_idx],
        "confidence": round(conf.item() * 100, 2),
        "probabilities": prob_dict
    }